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General Guidelines

Candidates for PhD degrees at Stanford must satisfactorily complete a program of study that includes 135 units of graduate course work and research. At least 3 units must be taken with each of four different Stanford faculty members. Study lists are submitted quarterly through Student AXESS with a total 10 units of coursework. Graduate students (including MD/PhD students in the graduate student phase of their training) must take all required courses for a letter grade. The university requires that you maintain a 3.0 GPA in order to remain enrolled at Stanford University.

Outline of Program Requirements

In addition to courses, students must also complete at least three lab rotations, pass qualifying exams, and participate in other program requirements before they earn their PhD.  Details on these additional requirements and the program training timeline may be found below.

Program Training Guideline

Course Descriptions

Advanced Undergraduate Courses

BIO 230:  Molecular and Cellular Immunology

Components of the immune system and their functions in immune responses in health and disease: development of the immune system; innate and adaptive immunity; structure and function of antibodies; molecular biology and biochemistry of antigen receptors and signaling pathways; cellular basis of immune responses and their regulation; genetic control of immune responses and disease susceptibility. Lectures and discussion in class and in sections. Satisfies Central Menu Areas 1 or 2. For upper class undergraduates and graduate students who have not previously taken an introductory immunology course. Prerequisite for undergraduates: Biology or Human Biology core, or consent of instructor.

Terms:  Aut | Units:  4


Required MCTI & CSI Foundational Courses

BIOS 200: Foundations in Experimental Biology

This course is divided into three 3-week cycles and is focused on the broad themes of Evolution, Energy and Information. During each cycle, students work in small teams and will be coached by faculty to develop an original research project and compose a brief written proposal explaining the research. Skills emphasized include: 1) reading for breadth and depth; 2) developing compelling, creative arguments; 3) communicating with the spoken and written word; 4) working in teams. Peer assessment and workshops; substantial face-to-face discussion with faculty drawn from across the Biosciences programs.

Terms: Aut | Units: 6

IMMUNOL 201:  Advanced Immunology I

For graduate students, medical students and advanced undergraduates. Topics include the innate and adaptive immune systems; genetics, structure, and function of immune molecules; lymphocyte activation and regulation of immune responses. Prerequisites: undergraduate course in Immunology and familiarity with experimental approaches in biochemistry, molecular biology, and cell biology.

Terms: Win | Units: 3

IMMUNOL 202: Advanced Immunology II

Readings of immunological literature. Classic problems and emerging areas based on primary literature. Student and faculty presentations. Prerequisite: IMMUNOL 201MI 211.

Terms: Spr | Units: 3

IMMUNOL 305: Immunology Journal Club

Required of first- to third-year graduate students. Graduate students present and discuss recent papers in the literature. May be repeated for credit.

Terms: Aut, Win, Spr | Units: 1

IMMUNOL 311: Seminar in Immunology

Enrollment limited to Ph.D., M.D./Ph.D., and medical students whose scholarly concentrations are in Immunology. Current research topics.

Terms: Aut, Win, Spr | Units: 1

BIO 141: Biostatistics

Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.

Terms: Aut | Units: 3-5

IMMUNOL 399: Graduate Research

For Ph.D., M.D./Ph.D. students, and medical students whose scholarly concentrations are in Immunology.

Terms: Aut, Win, Spr, Sum | Units: 1-15

IMMUNOL 290: Teaching in Immunology

Practical experience in teaching by serving as a teaching assistant in an immunology course. Unit values are allotted individually to reflect the level of teaching responsibility assigned to the student. May be repeated for credit.

Terms: Aut, Win, Spr, Sum | Units: 1-15

MED 255: The Responsible Conduct of Research

Forum. How to identify and approach ethical dilemmas that commonly arise in biomedical research. Issues in the practice of research such as in publication and interpretation of data, and issues raised by academic/industry ties. Contemporary debates at the interface of biomedical science and society regarding research on stem cells, bioweapons, genetic testing, human subjects, and vertebrate animals. Completion fulfills NIH/ADAMHA requirement for instruction in the ethical conduct of research. Prerequisite: research experience recommended.

Terms: Aut, Win, Spr | Units: 1


MCTI Foundational Courses

IMMUNOL 203: Advanced Immunology III

Key experiments and papers in immunology. Course focuses on the history of Immunology and how current research fits into the historical context. Students work on developing effective presentation skills.

Terms: Sum | Units: 2

BIO 141: Biostatistics

Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.

Terms: Aut | Units: 3-5

Choose ONE of the following:

MI 210:  Advanced Pathogenesis of Bacteria, Viruses, and Eukaryotic Parasties

For graduate and medical students, and advanced undergraduates; required of first-year graduate students in Microbiology and Immunology. The molecular mechanisms by which microorganisms invade animal and human hosts, express their genomes, interact with macromolecular pathways in the infected host, and induce disease. Current literature. Undergraduate students interested in taking this class must meet with the instructor to obtain approval before enrolling.

Terms: Win | Units: 4

BIO 214: Advanced Cell BIology

For Ph.D. students. Current research on cell structure, function, and dynamics. Topics include complex cell phenomena such as cell division, apoptosis, compartmentalization, transport and trafficking, motility and adhesion, and differentiation. Weekly reading of current papers from the primary literature. Preparation of an original research proposal. Prerequisite for advanced undergraduates: BIO 129A,B, and consent of instructor.

Terms: Win | Units: 4

IMMUNOL 206: Introduction to Applied Computational Tools in Immunology

Introduction to the major underpinnings of systems immunology: first principles of development of computational approaches to immunological questions and research; aspects of study design and analysis of data sets; literature and quantifying effects sizes as applied to clinical trial design. Final projects: individual and team reviewed grant proposals (1 unit); individual or team development of grant proposals into projects and journal articles (2 units).

Terms: Spr | Units: 1-2


MCTI Elective Courses

Students in the MCTI track are required to take at least ONE elective.

IMMUNOL 275: Tumor Immunology

Focuses on the ability of innate and adaptive immune responses to recognize and control tumor growth. Topics include: tumor antigens, tumor immunosurveillance and immunoediting, tumor immunotherapy, cancer vaccines and dendritic cell therapy. Tracks the historical developments of our understanding of modulating tumor immune response and discusses their relative significance in the light of current research findings. Prerequisite: for undergraduates, human biology or biology core.

Terms: TBA | Units: 3

CSB 210: Cell Signaling

The molecular mechanisms through which cells receive and respond to external signals. Emphasis is on principles of cell signaling, the systems-level properties of signal transduction modules, and experimental strategies through which cell signaling pathways are being studied. Prerequisite: working knowledge of biochemistry and genetics.

Terms: Win | Units: 4

SBIO 241: Biological Macromolecules

The physical and chemical basis of macromolecular function. Topics include: forces that stabilize macromolecular structure and their complexes; thermodynamics and statistical mechanics of macromolecular folding, binding, and allostery; diffusional processes; kinetics of enzymatic processes; the relationship of these principles to practical application in experimental design and interpretation. The class emphasizes interactive learning, and is divided equally among lectures, in-class group problem solving, and discussion of current and classical literature. Enrollment limited to 50. Prerequisites: Background in biochemistry and physical chemistry recommended but material available for those with deficiency in these areas; undergraduates with consent of instructor only.

Terms: Spr | Units: 3-5

DBIO 210: Developmental Biology

Current areas of research in developmental biology. How organismic complexity is generated during embryonic and post-embryonic development. The roles of genetic networks, induction events, cell lineage, maternal inheritance, cell-cell communication, and hormonal control in developmental processes in well-studied organisms such as vertebrates, insects, and nematodes. Team-taught. Students meet with faculty to discuss current papers from the literature. Prerequisite: graduate standing, consent of instructor. Recommended: familiarity with basic techniques and experimental rationales of molecular biology, biochemistry, and genetics.

Terms: Spr | Units: 4

CBIO 241: Molecular, Cellular, and Genetic Basis of Cancer

Core course required for first-year Cancer Biology graduate students. Focus is on key experiments and classic primary research papers in cancer biology. Letter grade required. Undergraduates require consent of course director.

Terms: Aut | Units: 4

IMMUNOL 204:  Innate Immunology

Innate immune mechanisms as the only defenses used by the majority of multicellular organisms. Topics include Toll signaling, NK cells, complement, antimicrobial peptides, phagocytes, neuroimmunity, community responses to infection, and the role of native flora in immunity. How microbes induce and defeat innate immune reactions, including examples from vertebrates, invertebrates, and plants.

Terms: TBA | Units: 3

IMMUNOL 206: Introduction to Applied Computational Tools in Immunology

(see above)

CSI Foundational Courses

CS 106X: Programming Abstractions (Accelerated)

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities.

Terms: Aut, Win | Units: 3-5

CS 109: Introduction to Probability for Computer Scientists

Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of MATH 51 or CME 100 or equivalent.

Terms: Aut, Win, Spr, Sum | Units: 3-5

CS 161: Design and Analysis of Algorithms

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching. Prerequisite: 103 or 103B; 109 or STATS 116.

Terms: Aut, Spr, Sum | Units: 3-5

IMMUNOL 206: Introduction to Applied Computational Tools in Immunology

(see above)

IMMUNOL 207: Essential Methods in Computational and Systems Immunology

Introduction to the major underpinnings of systems immunology: first principles of development of computational approaches to immunological questions and research; details of the algorithms and statistical principles underlying commonly used tools; aspects of study design and analysis of data sets. Prerequisites: CS106a and CS161 strongly recommended.

Terms: Spr | Units: 3

IMMUNOL 208: Advanced Computational and Systems Immunology


Terms: Aut | Units: 3

IMMUNOL 310: Seminars in Computational and Systems Immunology

Presentation of CSI technologies from recent literature. Discussion of emerging application areas and limitations. Dissemination of computational resources.

Terms: Spr, Sum | Units: 1

BIO 141: Biostatistics

(see above)

BIOMEDIN 212: Introduction to Biomedical Informatics Research Methodology

Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites: BIOMEDIN 210 or 211 or 214 or 217 or consent of instructor.

Terms: Spr | Units: 3

BIOE 214: Representations and Algorithms for Computational Molecular Biology

Topics: introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, Gibbs Sampling, basic structural computations on proteins, protein structure prediction, protein threading techniques, homology modeling, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, machine learning (clustering and classification), and natural language text processing. Prerequisites: programming skills; consent of instructor for 3 units.

Terms: Aut | Units: 3-4

CSI Elective Courses

Students in the CSI track must choose at least TWO electives for specialization.  In order to build their computational skill sets, CSI students may be advised to take additional courses by their thesis committees.

CME 263: Introduction to Linear Dynamical System

Applied linear algebra and linear dynamical systems with application to circuits, signal processing, communications, and control systems. Topics: least-squares approximations of over-determined equations and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singular value decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input/multi-output systems, impulse and step matrices; convolution and transfer matrix descriptions. Control, reachability, and state transfer; observability and least-squares state estimation. Prerequisites: linear algebra and matrices as in MATH 103; differential equations and Laplace transforms as in EE 102A.

Terms: Aut, Sum | Units: 3

CME 309:  Randomized Algorithms and Probabilistic Analysis

Randomness pervades the natural processes around us, from the formation of networks, to genetic recombination, to quantum physics. Randomness is also a powerful tool that can be leveraged to create algorithms and data structures which, in many cases, are more efficient and simpler than their deterministic counterparts. This course covers the key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Emphasis is on theoretical foundations, though we will apply this theory broadly, discussing applications in machine learning and data analysis, networking, and systems. Topics include tail bounds, the probabilistic method, Markov chains, and martingales, with applications to analyzing random graphs, metric embeddings, random walks, and a host of powerful and elegant randomized algorithms. Prerequisites: CS 161 and STAT 116, or equivalents and instructor consent.

Terms:  Aut | Units:  3

EE 278: Introduction to Statistical Signal Processing

Review of basic probability and random variables. Random vectors and processes; convergence and limit theorems; IID, independent increment, Markov, and Gaussian random processes; stationary random processes; autocorrelation and power spectral density; mean square error estimation, detection, and linear estimation. Formerly EE 278B. Prerequisites: EE178 and linear systems and Fourier transforms at the level of EE102A,B or EE261.

Terms: Aut, Spr, Sum | Units: 3

CME 206: Introduction to Numerical Methods for Engineering

Numerical methods from a user's point of view. Lagrange interpolation, splines. Integration: trapezoid, Romberg, Gauss, adaptive quadrature; numerical solution of ordinary differential equations: explicit and implicit methods, multistep methods, Runge-Kutta and predictor-corrector methods, boundary value problems, eigenvalue problems; systems of differential equations, stiffness. Emphasis is on analysis of numerical methods for accuracy, stability, and convergence. Introduction to numerical solutions of partial differential equations; Von Neumann stability analysis; alternating direction implicit methods and nonlinear equations. Prerequisites: CME 200ME 300ACME 204ME 300B.

Terms: Aut, Spr | Units: 3

STATS 202: Data Mining and Analysis

Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization. Prereqs: Probability at the level of Stats 116 and familiarity with linear algebra

Terms: Aut, Sum | Units: 3

CME 334: Advanced Methods in Numerical Optimization

Topics include interior-point methods, relaxation methods for nonlinear discrete optimization, sequential quadratic programming methods, optimal control and decomposition methods. Topic chosen in first class; different topics for individuals or groups possible. Individual or team projects. May be repeated for credit.

Terms: TBA | Units 3

STATS 217: Introduction to Stochastic Processes

Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. Non-Statistics masters students may want to consider taking STATS 215instead. Prerequisite: STATS 116 or consent of instructor.

Terms: Win, Sum | Units: 2-3

CME 364A: Convex Optimization I

Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as EE263, basic probability.

Terms: Win, Sum | Units: 3

BIOMEDIN 262:  Computational Genomics

Applications of computer science to genomics, and concepts in genomics from a computer science point of view. Topics: dynamic programming, sequence alignments, hidden Markov models, Gibbs sampling, and probabilistic context-free grammars. Applications of these tools to sequence analysis: comparative genomics, DNA sequencing and assembly, genomic annotation of repeats, genes, and regulatory sequences, microarrays and gene expression, phylogeny and molecular evolution, and RNA structure. Prerequisites: 161 or familiarity with basic algorithmic concepts. Recommended: basic knowledge of genetics.

Terms: Win | Units: 3

CME 372: Applied Fourier Analysis and Elements of Modern Signal Processing

Introduction to the mathematics of the Fourier transform and how it arises in a number of imaging problems. Mathematical topics include the Fourier transform, the Plancherel theorem, Fourier series, the Shannon sampling theorem, the discrete Fourier transform, and the spectral representation of stationary stochastic processes. Computational topics include fast Fourier transforms (FFT) and nonuniform FFTs. Applications include Fourier imaging (the theory of diffraction, computed tomography, and magnetic resonance imaging) and the theory of compressive sensing.

Terms: TBA | Units: 3

EE 376A: Information Theory

The fundamental ideas of information theory. Entropy and intrinsic randomness. Data compression to the entropy limit. Huffman coding. Arithmetic coding. Channel capacity, the communication limit. Gaussian channels. Kolmogorov complexity. Asymptotic equipartition property. Information theory and Kelly gambling. Applications to communication and data compression. Prerequisite: EE178 or STATS 116, or equivalent.

Terms: Win | Units: 3

BIOMED 260: Computational Methods for Biomedical Image Analysis and Interpretation 

The latest biological and medical imaging modalities and their applications in research and medicine. Focus is on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science. Includes a project. Enrollment for 3 units with reduced project requirements requires instructor consent. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. Knowledge of Matlab highly recommended.

Terms: Spr | Units: 3-4

BIOMEDIN 374: Algorithms in Biology

Algorithms and computational models applied to molecular biology and genetics. Topics vary annually. Possible topics include biological sequence comparison, annotation of genes and other functional elements, molecular evolution, genome rearrangements, microarrays and gene regulation, protein folding and classification, molecular docking, RNA secondary structure, DNA computing, and self-assembly. May be repeated for credit. Prerequisites: 161, 262 or 274, or BIOCHEM 218, or equivalents.

Terms: Spr | Units: 2-3

Welcome, New Students!

To learn more about the policies and requirements of the Immunology PhD program, navigate to the Resources Page and download the Immunology Graduate Handbook.